A chemical probe targeting the PWWP domain alters NSD2 nucleolar localization.

Nuclear receptor-binding SET domain-containing 2 (NSD2) is the primary enzyme responsible for the dimethylation of lysine 36 of histone 3 (H3K36), a mark associated with active gene transcription and intergenic DNA methylation. In addition to a methyltransferase domain, NSD2 harbors two proline-tryptophan-tryptophan-proline (PWWP) domains and five plant homeodomains (PHDs) believed to serve as chromatin reading modules. Here, we report a chemical probe targeting the N-terminal PWWP (PWWP1) domain of NSD2. UNC6934 occupies the canonical H3K36me2-binding pocket of PWWP1, antagonizes PWWP1 interaction with nucleosomal H3K36me2 and selectively engages endogenous NSD2 in cells. UNC6934 induces accumulation of endogenous NSD2 in the nucleolus, phenocopying the localization defects of NSD2 protein isoforms lacking PWWP1 that result from translocations prevalent in multiple myeloma (MM). Mutations of other NSD2 chromatin reader domains also increase NSD2 nucleolar localization and enhance the effect of UNC6934. This chemical probe and the accompanying negative control UNC7145 will be useful tools in defining NSD2 biology.

Off-Pocket Activity Cliffs: A Puzzling Facet of Molecular Recognition.

While accurate quantitative prediction of ligand-protein binding affinity remains an elusive goal, high-affinity ligands to therapeutic targets are being designed through heuristic optimization of ligand-protein contacts. However, herein, through large-scale data mining and analyses, we demonstrate that a ligand's binding can also be strongly affected through modifying its solvent-exposed portion that does not make contacts with the protein, thus resulting in "off-pocket activity cliffs" (OAC). We then exposed the roots of the OAC phenomenon by means of molecular dynamics (MD) simulations and MD data analyses. We expect OAC to extend our knowledge of molecular recognition and enhance the drug designer's toolkit.

Site of Tagging Influences the Ochratoxin Recognition by Peptide NFO4: A Molecular Dynamics Study.

Molecular recognition by synthetic peptides is growing in importance in the design of biosensing elements used in the detection and monitoring of a wide variety of hapten bioanlaytes. Conferring specificity via bioimmobilization and subsequent recovery and purification of such sensing elements are aided by the use of affinity tags. However, the tag and its site of placement can potentially compromise the hapten recognition capabilities of the peptide, necessitating a detailed experimental characterization and optimization of the tagged molecular recognition entity. The objective of this study was to assess the impact of site-specific tags on a native peptide's fold and hapten recognition capabilities using an advanced molecular dynamics (MD) simulation approach involving bias-exchange metadynamics and Markov State Models. The in-solution binding preferences of affinity tagged NFO4 (VYMNRKYYKCCK) to chlorinated (OTA) and non-chlorinated (OTB) analogues of ochratoxin were evaluated by appending hexa-histidine tags (6× His-tag) to the peptide's N-terminus (NterNFO4) or C-terminus (CterNFO4), respectively. The untagged NFO4 (NFO4), previously shown to bind with high affinity and selectivity to OTA, served as the control. Results indicate that the addition of site-specific 6× His-tags altered the peptide's native fold and the ochratoxin binding mechanism, with the influence of site-specific affinity tags being most evident on the peptide's interaction with OTA. The tags at the N-terminus of NFO4 preserved the native fold and actively contributed to the nonbonded interactions with OTA. In contrast, the tags at the C-terminus of NFO4 altered the native fold and were agnostic in its nonbonded interactions with OTA. The tags also increased the penalty associated with solvating the peptide-OTA complex. Interestingly, the tags did not significantly influence the nonbonded interactions or the penalty associated with solvating the peptide-OTB complex. Overall, the combined contributions of nonbonded interaction and solvation penalty were responsible for the retention of the native hapten recognition capabilities in NterNFO4 and compromised native recognition capabilities in CterNFO4. Advanced MD approaches can thus provide structural and energetic insights critical to evaluate the impact of site-specific tags and may aid in the selection and optimization of the binding preferences of a specific biosensing element.

Cluster analysis of molecular simulation trajectories for systems where both conformation and orientation of the sampled states are important.

Clustering methods have been widely used to group together similar conformational states from molecular simulations of biomolecules in solution. For applications such as the interaction of a protein with a surface, the orientation of the protein relative to the surface is also an important clustering parameter because of its potential effect on adsorbed-state bioactivity. This study presents cluster analysis methods that are specifically designed for systems where both molecular orientation and conformation are important, and the methods are demonstrated using test cases of adsorbed proteins for validation. Additionally, because cluster analysis can be a very subjective process, an objective procedure for identifying both the optimal number of clusters and the best clustering algorithm to be applied to analyze a given dataset is presented. The method is demonstrated for several agglomerative hierarchical clustering algorithms used in conjunction with three cluster validation techniques. © 2016 Wiley Periodicals, Inc.

Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors.

Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data is available. In the best case, crystal structures of a ligand-bound protein can help find more potent ligands. However, virtual screens tend to be less predictive when only ligand-free crystal structures are available, and even less predictive if a homology model or other predicted structure must be used. Here, we explore the possibility that this situation can be improved by better accounting for protein dynamics, as simulations started from a single structure have a reasonable chance of sampling nearby structures that are more compatible with ligand binding. As a specific example, we consider the cancer drug target PPM1D/Wip1 phosphatase, a protein that lacks crystal structures. High-throughput screens have led to the discovery of several allosteric inhibitors of PPM1D, but their binding mode remains unknown. To enable further drug discovery efforts, we assessed the predictive power of an AlphaFold-predicted structure of PPM1D and a Markov state model (MSM) built from molecular dynamics simulations initiated from that structure. Our simulations reveal a cryptic pocket at the interface between two important structural elements, the flap and hinge regions. Using deep learning to predict the pose quality of each docked compound for the active site and cryptic pocket suggests that the inhibitors strongly prefer binding to the cryptic pocket, consistent with their allosteric effect. The predicted affinities for the dynamically uncovered cryptic pocket also recapitulate the relative potencies of the compounds (τ b =0.70) better than the predicted affinities for the static AlphaFold-predicted structure (τ b =0.42). Taken together, these results suggest that targeting the cryptic pocket is a good strategy for drugging PPM1D and, more generally, that conformations selected from simulation can improve virtual screening when limited structural data is available.

Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors.

Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data is available. In the best case, crystal structures of a ligand-bound protein can help find more potent ligands. However, virtual screens tend to be less predictive when only ligand-free crystal structures are available, and even less predictive if a homology model or other predicted structure must be used. Here, we explore the possibility that this situation can be improved by better accounting for protein dynamics, as simulations started from a single structure have a reasonable chance of sampling nearby structures that are more compatible with ligand binding. As a specific example, we consider the cancer drug target PPM1D/Wip1 phosphatase, a protein that lacks crystal structures. High-throughput screens have led to the discovery of several allosteric inhibitors of PPM1D, but their binding mode remains unknown. To enable further drug discovery efforts, we assessed the predictive power of an AlphaFold-predicted structure of PPM1D and a Markov state model (MSM) built from molecular dynamics simulations initiated from that structure. Our simulations reveal a cryptic pocket at the interface between two important structural elements, the flap and hinge regions. Using deep learning to predict the pose quality of each docked compound for the active site and cryptic pocket suggests that the inhibitors strongly prefer binding to the cryptic pocket, consistent with their allosteric effect. The predicted affinities for the dynamically uncovered cryptic pocket also recapitulate the relative potencies of the compounds (τb = 0.70) better than the predicted affinities for the static AlphaFold-predicted structure (τb = 0.42). Taken together, these results suggest that targeting the cryptic pocket is a good strategy for drugging PPM1D and, more generally, that conformations selected from simulation can improve virtual screening when limited structural data is available.

An atypical heterotrimeric Gα protein has substantially reduced nucleotide binding but retains nucleotide-independent interactions with its cognate RGS protein and Gßγ dimer.

Plants uniquely have a family of proteins called extra-large G proteins (XLG) that share homology in their C-terminal half with the canonical Gα subunits; we carefully detail here that Arabidopsis XLG2 lacks critical residues requisite for nucleotide binding and hydrolysis which is consistent with our quantitative analyses. Based on microscale thermophoresis, Arabidopsis XLG2 binds GTPγS with an affinity 100 times lower than that to canonical Gα subunits. This means that given the concentration range of guanine nucleotide in plant cells, XLG2 is not likely bound by GTP in vivo. Homology modeling and molecular dynamics simulations provide a plausible mechanism for the poor nucleotide binding affinity of XLG2. Simulations indicate substantially stronger salt bridge networks formed by several key amino-acid residues of AtGPA1 which are either misplaced or missing in XLG2. These residues in AtGPA1 not only maintain the overall shape and integrity of the apoprotein cavity but also increase the frequency of favorable nucleotide-protein interactions in the nucleotide-bound state. Despite this loss of nucleotide dependency, XLG2 binds the RGS domain of AtRGS1 with an affinity similar to the Arabidopsis AtGPA1 in its apo-state and about 2 times lower than AtGPA1 in its transition state. In addition, XLG2 binds the Gßγ dimer with an affinity similar to that of AtGPA1. XLG2 likely acts as a dominant negative Gα protein to block G protein signaling. We propose that XLG2, independent of guanine nucleotide binding, regulates the active state of the canonical G protein pathway directly by sequestering Gßγ and indirectly by promoting heterodimer formation.Communicated by Ramaswamy H. Sarma.

Discovery of selective activators of PRC2 mutant EED-I363M.

Many common disease-causing mutations result in loss-of-function (LOF) of the proteins in which they occur. LOF mutations have proven recalcitrant to pharmacologic intervention, presenting a challenge for the development of targeted therapeutics. Polycomb repressive complex 2 (PRC2), which contains core subunits (EZH2, EED, and SUZ12), regulates gene activity by trimethylation of histone 3 lysine 27. The dysregulation of PRC2 catalytic activity by mutations has been implicated in cancer and other diseases. Among the mutations that cause PRC2 malfunction, an I363M LOF mutation of EED has been identified in myeloid disorders, where it prevents allosteric activation of EZH2 catalysis. We describe structure-based design and computational simulations of ligands created to ameliorate this LOF. Notably, these compounds selectively stimulate the catalytic activity of PRC2-EED-I363M over wildtype-PRC2. Overall, this work demonstrates the feasibility of developing targeted therapeutics for PRC2-EED-I363M that act as allosteric agonists, potentially correcting this LOF mutant phenotype.

Application of advanced sampling and analysis methods to predict the structure of adsorbed protein on a material surface.

The use of standard molecular dynamics simulation methods to predict the interactions of a protein with a material surface have the inherent limitations of lacking the ability to determine the most likely conformations and orientations of the adsorbed protein on the surface and to determine the level of convergence attained by the simulation. In addition, standard mixing rules are typically applied to combine the nonbonded force field parameters of the solution and solid phases the system to represent interfacial behavior without validation. As a means to circumvent these problems, the authors demonstrate the application of an efficient advanced sampling method (TIGER2A) for the simulation of the adsorption of hen egg-white lysozyme on a crystalline (110) high-density polyethylene surface plane. Simulations are conducted to generate a Boltzmann-weighted ensemble of sampled states using force field parameters that were validated to represent interfacial behavior for this system. The resulting ensembles of sampled states were then analyzed using an in-house-developed cluster analysis method to predict the most probable orientations and conformations of the protein on the surface based on the amount of sampling performed, from which free energy differences between the adsorbed states were able to be calculated. In addition, by conducting two independent sets of TIGER2A simulations combined with cluster analyses, the authors demonstrate a method to estimate the degree of convergence achieved for a given amount of sampling. The results from these simulations demonstrate that these methods enable the most probable orientations and conformations of an adsorbed protein to be predicted and that the use of our validated interfacial force field parameter set provides closer agreement to available experimental results compared to using standard CHARMM force field parameterization to represent molecular behavior at the interface.

Parameterization of an interfacial force field for accurate representation of peptide adsorption free energy on high-density polyethylene.

Interfacial force field (IFF) parameters for use with the CHARMM force field have been developed for interactions between peptides and high-density polyethylene (HDPE). Parameterization of the IFF was performed to achieve agreement between experimental and calculated adsorption free energies of small TGTG-X-GTGT host-guest peptides (T = threonine, G = glycine, and X = variable amino-acid residue) on HDPE, with ±0.5 kcal/mol agreement. This IFF parameter set consists of tuned nonbonded parameters (i.e., partial charges and Lennard-Jones parameters) for use with an in-house-modified CHARMM molecular dynamic program that enables the use of an independent set of force field parameters to control molecular behavior at a solid-liquid interface. The R correlation coefficient between the simulated and experimental peptide adsorption free energies increased from 0.00 for the standard CHARMM force field parameters to 0.88 for the tuned IFF parameters. Subsequent studies are planned to apply the tuned IFF parameter set for the simulation of protein adsorption behavior on an HDPE surface for comparison with experimental values of adsorbed protein orientation and conformation.

Development of a tuned interfacial force field parameter set for the simulation of protein adsorption to silica glass.

Adsorption free energies for eight host-guest peptides (TGTG-X-GTGT, with X = N, D, G, K, F, T, W, and V) on two different silica surfaces [quartz (100) and silica glass] were calculated using umbrella sampling and replica exchange molecular dynamics and compared with experimental values determined by atomic force microscopy. Using the CHARMM force field, adsorption free energies were found to be overestimated (i.e., too strongly adsorbing) by about 5-9 kcal/mol compared to the experimental data for both types of silica surfaces. Peptide adsorption behavior for the silica glass surface was then adjusted using a modified version of the CHARMM program, which we call dual force-field CHARMM, which allows separate sets of nonbonded parameters (i.e., partial charge and Lennard-Jones parameters) to be used to represent intra-phase and inter-phase interactions within a given molecular system. Using this program, interfacial force field (IFF) parameters for the peptide-silica glass systems were corrected to obtain adsorption free energies within about 0.5 kcal/mol of their respective experimental values, while IFF tuning for the quartz (100) surface remains for future work. The tuned IFF parameter set for silica glass will subsequently be used for simulations of protein adsorption behavior on silica glass with greater confidence in the balance between relative adsorption affinities of amino acid residues and the aqueous solution for the silica glass surface.